Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel
Notice bibliographique
Résumé
Abstract BioHansel performs high-resolution genotyping of bacterial isolates by identifying phylogenetically informative single nucleotide polymorphisms (SNPs), also known as canonical SNPs, in whole genome sequencing (WGS) data. The application uses a fast k -mer matching algorithm to map pathogen WGS data to canonical SNPs contained in hierarchically structured schemas and assigns genotypes based on the detected SNP profile. Using modest computing resources, BioHansel efficiently types isolates from raw sequence reads or assembled contigs in a matter of seconds, making it attractive for use by public health, food safety, environmental, and agricultural authorities that wish to apply WGS methodologies for their surveillance, diagnostics, and research programs. BioHansel currently provides canonical SNP genotyping schemas for four prevalent Salmonella serovars—Typhi, Typhimurium, Enteritidis and Heidelberg—as well as a schema for Mycobacterium tuberculosis . Users can also supply their own schemas for genotyping other organisms. BioHansel’s quality assurance system assesses the validity of the genotyping results and can identify low quality data, contaminated datasets, and misidentified organisms. BioHansel is targeted to support surveillance, source attribution, risk assessment, diagnostics, and rapid screening for public health purposes, such as product recalls. BioHansel is an open source application with packages available for PyPI, Conda, and the Galaxy workflow manager. In summary, BioHansel performs efficient, rapid, accurate, and high-resolution classification of bacterial genomes from sequence reads or assembled contigs on standard computing hardware. BioHansel is suitable for use as a general research tool as well as in fully operationalized WGS workflows at the front lines of infectious disease surveillance, diagnostics, and outbreak investigation and response. Impact statement Public health, food safety, environmental, and agricultural authorities are currently engaged in a global effort to incorporate whole genome sequencing technologies into their infectious disease research, surveillance, and outbreak investigation programs. Its widespread adoption, however, has been impeded by two major obstacles: the need for high performance computing to generate results and the expert knowledge required to interpret and communicate those results. BioHansel addresses these limitations by rapidly genotyping pathogens from whole genome sequence data in an accurate, simple, familiar, and easily sharable manner using standard computing resources. BioHansel provides a compact and readily interpretable genotype based on canonical SNP genotyping schemas. BioHansel’s genotyping nomenclature encodes the pathogen’s position in its population structure, which simplifies and facilitates its comparison with actively circulating strains and historical strains. The genotyping information provided by BioHansel can identify points of intervention to prevent the spread of pathogenic bacteria, screen for the presence of priority pathogens, and perform source attribution and risk assessment. Thus, BioHansel serves as a readily accessible and powerful WGS method, implementable on a laptop, for genotyping pathogens to detect, monitor, and control the emergence and spread of infectious disease through surveillance, screening, diagnostics, and outbreak investigation and response activities. Data summary BioHansel is a Python 3 application available as PyPI, Conda Galaxy Tool Shed packages. It is an open source application distributed under the Apache License, Version 2.0. Source code is available at https://github.com/phac-nml/biohansel . The BioHansel user guide is available at https://bio-hansel.readthedocs.io/en/readthedocs/ . Supplementary Materials are available at https://github.com/phac-nml/biohansel-manuscript-supplementary-data . The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».